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Safety Risk Recognition Method Based on Abnormal Scenarios
Construction safety monitoring is a significant issue in practical engineering. Unfortunately, specific techniques in this field still heavily depend on artificial monitoring. To detect the abnormal scenarios during the construction process automatically, a method was proposed for the detection and localization of abnormal scenarios in time and space. The method consists of three components: (1) an I3D-AE video prediction model, which extracts the video features from multiple I3Ds and reconstructs the video by 3D deconvolution; (2) a spatial localization module AS-CAM, which determines the location of abnormal areas via back-propagating the I3D-AE; (3) a temporal parameter St, which can calculate the abnormal time period. The effectiveness of the method was verified with the use of a dataset, and the resulting data were plotted as ROC curves. The results indicated that the proposed method exceeded 0.9 on the frame-level test and 0.76 on the pixel-level test with the use of the AUC evaluation metric. Therefore, it can be used to assist the construction managers to improve the efficiency of construction safety management.
Safety Risk Recognition Method Based on Abnormal Scenarios
Construction safety monitoring is a significant issue in practical engineering. Unfortunately, specific techniques in this field still heavily depend on artificial monitoring. To detect the abnormal scenarios during the construction process automatically, a method was proposed for the detection and localization of abnormal scenarios in time and space. The method consists of three components: (1) an I3D-AE video prediction model, which extracts the video features from multiple I3Ds and reconstructs the video by 3D deconvolution; (2) a spatial localization module AS-CAM, which determines the location of abnormal areas via back-propagating the I3D-AE; (3) a temporal parameter St, which can calculate the abnormal time period. The effectiveness of the method was verified with the use of a dataset, and the resulting data were plotted as ROC curves. The results indicated that the proposed method exceeded 0.9 on the frame-level test and 0.76 on the pixel-level test with the use of the AUC evaluation metric. Therefore, it can be used to assist the construction managers to improve the efficiency of construction safety management.
Safety Risk Recognition Method Based on Abnormal Scenarios
Ziqi Li (author) / Bo Song (author) / Dongsheng Li (author)
2022
Article (Journal)
Electronic Resource
Unknown
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